Total Number of Trials = 104 Sweet taste and bitter taste was selected by participant to reflect reward & punishment
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(reshape)
##
## Attaching package: 'reshape'
## The following objects are masked from 'package:tidyr':
##
## expand, smiths
## The following object is masked from 'package:dplyr':
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data <- read.delim("~/Documents/bevel_choice/all_subjects.txt")
names(data) <- c("subj", "run", "pair", "choice", "outcome", "congruent", "RT")
data <- data %>%
group_by(.dots=c("subj","pair")) %>%
mutate(Count=row_number())
percent_correct_by_trial_ab <- function(n) {
count <- sum(data$Count == n & data$choice=="corr" & data$pair == "12")
countall <- sum(data$Count == n & data$pair == "12")
return(count/countall)
}
percent_correct_by_trial_cd <- function(n) {
count <- sum(data$Count == n & data$choice=="corr" & data$pair == "34")
countall <- sum(data$Count == n & data$pair == "34")
return(count/countall)
}
percent_correct_by_trial_ef <- function(n) {
count <- sum(data$Count == n & data$choice=="corr" & data$pair == "56")
countall <- sum(data$Count == n & data$pair == "56")
return(count/countall)
}
#percent_correct_by_trial_ab(10)
x <- 1:46
output_ab <- lapply(x, percent_correct_by_trial_ab)
output_cd <- lapply(x, percent_correct_by_trial_cd)
output_ef <- lapply(x, percent_correct_by_trial_ef)
df_ab <- data.frame(matrix(unlist(output_ab), nrow=length(output_ab), byrow=T))
df_cd <- data.frame(matrix(unlist(output_cd), nrow=length(output_cd), byrow=T))
df_ef <- data.frame(matrix(unlist(output_ef), nrow=length(output_ef), byrow=T))
colnames(df_ab)[colnames(df_ab)=="matrix.unlist.output_ab...nrow...length.output_ab...byrow...T."] <- "percent_correct_ab"
colnames(df_cd)[colnames(df_cd)=="matrix.unlist.output_cd...nrow...length.output_cd...byrow...T."] <- "percent_correct_cd"
colnames(df_ef)[colnames(df_ef)=="matrix.unlist.output_ef...nrow...length.output_ef...byrow...T."] <- "percent_correct_ef"
df_ab$trialnum<-row.names(df_ab)
df_cd$trialnum<-row.names(df_cd)
df_ef$trialnum<-row.names(df_ef)
#head(df_ab$trialnum)
data0<-merge(df_ab, df_cd, by="trialnum")
data1<- merge(data0, df_ef, by="trialnum")
df_ab$trial <- seq.int(nrow(df_ab))
df_cd$trial <- seq.int(nrow(df_cd))
df_ef$trial <- seq.int(nrow(df_ef))
plot1 <- ggplot(data=df_ab, aes(x=trial, y=percent_correct_ab, group=1)) +
geom_line()+
geom_point() +
theme_classic() + scale_x_continuous(name="Trial Number") +
scale_y_continuous(name="Percent of Sample choosing 80% Shape")
plot2 <- ggplot(data=df_cd, aes(x=trial, y=percent_correct_cd, group=1)) +
geom_line()+
geom_point() +
theme_classic() + scale_x_continuous(name="Trial Number") +
scale_y_continuous(name="Percent of Sample choosing 70% Shape")
plot3 <- ggplot(data=df_ef, aes(x=trial, y=percent_correct_ef, group=1)) +
geom_line()+
geom_point() +
theme_classic() + scale_x_continuous(name="Trial Number") +
scale_y_continuous(name="Percent of Sample choosing 60% Shape")
plot1
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_point).
plot2
## Warning: Removed 3 rows containing missing values (geom_path).
## Warning: Removed 3 rows containing missing values (geom_point).
plot3
ggplot(data1, aes(as.numeric(trialnum))) +
geom_line(aes(y = percent_correct_ab, colour = "80/20 pair")) +
geom_line(aes(y = percent_correct_cd, colour = "70/30 pair")) +
geom_line(aes(y = percent_correct_ef, colour = "60/40 pair")) +
theme_classic() + scale_x_continuous(name="Trial Number") +
scale_y_continuous(name="Percent of Sample choosing Higher %Correct Shape") +
labs(colour = "Shape Pair")
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 3 rows containing missing values (geom_path).
library(ggplot2)
library(ggpubr)
## Warning: package 'ggpubr' was built under R version 3.5.2
## Loading required package: magrittr
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:tidyr':
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library(plyr)
## -------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## -------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following object is masked from 'package:ggpubr':
##
## mutate
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##
## rename, round_any
## The following objects are masked from 'package:dplyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ tibble 1.4.2 ✔ purrr 0.2.5
## ✔ readr 1.1.1 ✔ stringr 1.3.1
## ✔ tibble 1.4.2 ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ plyr::arrange() masks dplyr::arrange()
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## ✖ plyr::summarise() masks dplyr::summarise()
## ✖ plyr::summarize() masks dplyr::summarize()
library(reshape)
library(data.table)
## Warning: package 'data.table' was built under R version 3.5.2
##
## Attaching package: 'data.table'
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80-20 == 12 70-30 == 34 60-40 == 56
sub1<-read.table("~/Documents/bevel_choice/by_participant_txtfiles/50.txt", sep="\t", header=F)
head(sub1)
names(sub1)<-c("sub_num","run","type","choice","side","outcome","congruent","RT")
head(sub1)
sub1$side<-as.factor(sub1$side)
sub1$side<-revalue(sub1$side, c("1"="left", "2"="right"))
sub1$trial<-row.names(sub1)
head(sub1)
cbPalette <- c("00900","FF3300")
plot1<-ggplot(subset(sub1, type == "EF"), aes(trial,side, group=as.factor(side)))+
geom_line()+
geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)), size=5)+
scale_fill_manual(cbPalette)+
theme_classic()
plot2<-ggplot(subset(sub1, type == "CD"), aes(trial,side, group=as.factor(side)))+
geom_line()+
geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)), size=5)+
scale_fill_manual(cbPalette)+
theme_classic()
plot3<-ggplot(subset(sub1, type == "AB"), aes(trial,side, group=as.factor(side)))+
geom_line()+
geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)), size=5)+
geom_point(aes(shape=as.factor(choice)))+
theme_classic()+
scale_fill_manual(cbPalette)
ggarrange(plot1,plot2,plot3,
labels = c("60-40", "70-30","80-20"),
ncol = 1, nrow = 3)
plot3<-ggplot(subset(sub1, type == "AB"), aes(as.numeric(trial),side, group=as.factor(side)))+
geom_line()+
geom_point(aes(color=as.factor(congruent), shape=as.factor(outcome)), size=10)+
geom_point(aes(shape=as.factor(choice), color=as.factor(congruent)), size=5)+
theme_classic()+
scale_fill_manual(cbPalette)
plot3
Left right, not informative.
x<-subset(sub1, type == "AB")
summary(as.factor(x$choice))
## A B C D E F Miss
## 21 18 0 0 0 0 0
summary(x)
## sub_num run type choice side outcome
## Min. :50 run01: 6 AB:39 A :21 left :27 Miss : 0
## 1st Qu.:50 run02:15 CD: 0 B :18 right:12 punish:23
## Median :50 run03: 8 EF: 0 C : 0 Miss : 0 reward:16
## Mean :50 run04:10 D : 0
## 3rd Qu.:50 E : 0
## Max. :50 F : 0
## Miss: 0
## congruent RT trial
## matched :30 1.567835: 1 Length:39
## mismatched: 9 1.581710: 1 Class :character
## Miss : 0 1.583164: 1 Mode :character
## 1.583882: 1
## 1.584442: 1
## 1.584814: 1
## (Other) :33
data$ov_ob[BMI<18.5]<-“Underweight”
sub1$outcome0[sub1$outcome == "Miss"] <- 0
sub1$outcome0[sub1$outcome == "punish"] <- -10
sub1$outcome0[sub1$outcome == "reward"] <- 10
hm1<-ggplot(sub1,aes(as.numeric(trial), sub_num ,fill=outcome0))+
geom_tile()+
scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+
geom_point(aes(shape=as.factor(choice), size=10, color=as.factor(choice)))
hm1
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.
## Warning: Removed 1 rows containing missing values (geom_point).
readdata <- function(fn){
dt_temp <- fread(fn, sep="\t")
return(dt_temp)
}
all.files <- list.files(path = "~/Documents/bevel_choice/by_participant_txtfiles/",pattern = ".txt", full.names = TRUE)
mylist <- lapply(all.files, readdata)
mydata <- rbindlist(mylist, use.names=FALSE)
names(mydata)<-c("sub_num","run","type","choice","side","outcome","congruent","RT")
mydata$side<-as.factor(mydata$side)
mydata$side<-revalue(mydata$side, c("1"="left", "2"="right"))
mydata <- mydata %>%
group_by(.dots=c("sub_num")) %>%
dplyr::mutate(Count=row_number())
head(mydata)
mydata$sub_num
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## [9086] 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90
mydata$outcome0[mydata$outcome == "Miss"] <- 0
## Warning: Unknown or uninitialised column: 'outcome0'.
mydata$outcome0[mydata$outcome == "punish"] <- -10
mydata$outcome0[mydata$outcome == "reward"] <- 10
hmTOTAL<-ggplot(mydata,aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
geom_tile()+
scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+theme_classic()+
geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
hmTOTAL
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.
## Warning: Removed 482 rows containing missing values (geom_point).
more_data<-read.table("~/Documents/bevel_choice/clean_bevel.csv",header=T, sep=",")
more_data$sub_num<-row.names(more_data)
head(more_data$sub_num)
## [1] "1" "2" "3" "4" "5" "6"
data0<-merge(mydata, more_data, by="sub_num")
head(data0)
names(data0)
## [1] "sub_num" "run"
## [3] "type" "choice"
## [5] "side" "outcome"
## [7] "congruent" "RT"
## [9] "Count" "outcome0"
## [11] "ID" "intials"
## [13] "date" "weight"
## [15] "height" "BMI"
## [17] "BMI_cat" "hba1c"
## [19] "bloodglucose" "bitter"
## [21] "age" "DOB"
## [23] "hispanic" "race1"
## [25] "sex" "mens_date"
## [27] "mens_length" "sensitivity_reward"
## [29] "sensitivity_punish" "test_result_group"
## [31] "preTTfullness" "preTThunger"
## [33] "preTTthirst" "hourssincelastmeal"
## [35] "sweetstim_level" "sweetstim_pleasent"
## [37] "sweetstim_desire" "sweetstim_intense"
## [39] "sweetstim_bitter" "sweetstim_sweet"
## [41] "bitterstim_level" "bitterstim_pleasent"
## [43] "bitterstim_desire" "bitterstim_intense"
## [45] "bitterstim_bitter" "bitterstim_sweet"
## [47] "sweet1pleasent" "sweet1desire"
## [49] "sweet1intense" "sweet1bitter"
## [51] "sweet1sweet" "sweet2pleasent"
## [53] "sweet2desire" "sweet2intense"
## [55] "sweet2bitter" "sweet2sweet"
## [57] "sweet3pleasent" "sweet3desire"
## [59] "sweet3intense" "sweet3bitter"
## [61] "sweet3sweet" "sweet4pleasent"
## [63] "sweet4desire" "sweet4intense"
## [65] "sweet4bitter" "sweet4sweet"
## [67] "sweet1rank" "sweet2rank"
## [69] "sweet3rank" "sweet4rank"
## [71] "bitter1pleasent" "bitter1desire"
## [73] "bitter1intense" "bitter1bitter"
## [75] "bitter1sweet" "bitter2pleasent"
## [77] "bitter2desire" "bitter2intense"
## [79] "bitter2bitter" "bitter2sweet"
## [81] "bitter3pleasent" "bitter3desire"
## [83] "bitter3intense" "bitter3bitter"
## [85] "bitter3sweet" "bitter4pleasent"
## [87] "bitter4desire" "bitter4intense"
## [89] "bitter4bitter" "bitter4sweet"
## [91] "bitter1rank" "bitter2rank"
## [93] "bitter3rank" "bitter4rank"
## [95] "FFQ1" "FFQ2"
## [97] "FFQ3" "FFQ4"
## [99] "FFQ5" "FFQ6"
## [101] "FFQ7" "FFQ8"
## [103] "FFQ9" "FFQ10"
## [105] "FFQ11" "FFQ12"
## [107] "FFQ13" "FFQ14"
## [109] "FFQ15" "FFQ16"
## [111] "FFQ17" "FFQ18"
## [113] "FFQ19" "FFQ20"
## [115] "FFQ21" "FFQ22"
## [117] "FFQ23" "FFQ24"
## [119] "FFQ25" "FFQ26"
## [121] "FFQ27" "FFQ28"
## [123] "FFQ29" "FFQ30"
## [125] "FFQ31" "FFQ32"
## [127] "FFQ33" "FFQ34"
## [129] "FFQ35" "FFQ36"
## [131] "FFQ37" "FFQ38"
## [133] "FFQ39" "FFQ40"
## [135] "FFQ41" "FFQ42"
## [137] "FFQ43" "FFQ44"
## [139] "FFQ45" "FFQ46"
## [141] "FFQ47" "FFQ48"
## [143] "FFQ49" "FFQ50"
## [145] "FFQ51" "FFQ52"
## [147] "FFQ53" "FFQ54"
## [149] "FFQ55" "FFQ56"
## [151] "FFQ57" "FFQ58"
## [153] "FFQ59" "FFQ60"
## [155] "BIQ1" "BIQ2"
## [157] "BIQ3" "BIQ4"
## [159] "BIQ5" "BIQ6"
## [161] "BIQ7" "BIQ8"
## [163] "BIQ9" "BIQ10"
## [165] "BIQ11" "BIQ12"
## [167] "BIQ13" "BIQ14"
## [169] "BIQ15" "BIQ16"
## [171] "SPSRQ1" "SPSRQ2"
## [173] "SPSRQ3" "SPSRQ4"
## [175] "SPSRQ5" "SPSRQ6"
## [177] "SPSRQ7" "SPSRQ8"
## [179] "SPSRQ9" "SPSRQ10"
## [181] "SPSRQ11" "SPSRQ12"
## [183] "SPSRQ13" "SPSRQ14"
## [185] "SPSRQ15" "SPSRQ16"
## [187] "SPSRQ17" "SPSRQ18"
## [189] "SPSRQ19" "SPSRQ20"
## [191] "DEBQ1" "DEBQ2"
## [193] "DEBQ3" "DEBQ4"
## [195] "DEBQ5" "DEBQ6"
## [197] "DEBQ7" "DEBQ8"
## [199] "DEBQ9" "DEBQ10"
## [201] "DEBQ11" "DEBQ12"
## [203] "DEBQ13" "DEBQ14"
## [205] "DEBQ15" "DEBQ16"
## [207] "DEBQ17" "DEBQ18"
## [209] "DEBQ19" "DEBQ20"
## [211] "DEBQ21" "DEBQ22"
## [213] "DEBQ23" "DEBQ24"
## [215] "DEBQ25" "DEBQ26"
## [217] "DEBQ27" "DEBQ28"
## [219] "DEBQ29" "DEBQ30"
## [221] "DEBQ31" "DEBQ32"
## [223] "DEBQ33" "BISBAS1"
## [225] "BISBAS2" "BISBAS3"
## [227] "BISBAS4" "BISBAS5"
## [229] "BISBAS7" "BISBAS8"
## [231] "BISBAS9" "BISBAS10"
## [233] "BISBAS11" "BISBAS12"
## [235] "BISBAS13" "BISBAS14"
## [237] "BISBAS15" "BISBAS16"
## [239] "BISBAS17" "BISBAS18"
## [241] "BISBAS19" "BISBAS20"
## [243] "BISBAS21" "BISBAS22"
## [245] "BISBAS23" "BISBAS24"
## [247] "IPAQ1" "IPAQ2"
## [249] "IPAQ3" "IPAQ4"
## [251] "IPAQ5" "IPAQ6"
## [253] "IPAQ7" "IPAQ8"
## [255] "IPAQ9" "IPAQ10"
## [257] "IPAQ11" "IPAQ12"
## [259] "IPAQ13" "IPAQ14"
## [261] "IPAQ15" "IPAQ16"
## [263] "IPAQ17" "IPAQ18"
## [265] "IPAQ19" "IPAQ20"
## [267] "IPAQ21" "IPAQ22"
## [269] "IPAQ23" "IPAQ24"
## [271] "IPAQ25" "IPAQ26"
## [273] "IPAQ27" "IPAQ28"
## [275] "IPAQ29" "IPAQ30"
## [277] "IPAQ31" "IPAQ32"
## [279] "IPAQ33" "IPAQ34"
## [281] "IPAQ35" "IPAQ36"
## [283] "IPAQ37" "IPAQ38"
## [285] "IPAQ39" "IPAQ40"
## [287] "IPAQ41" "IPAQ42"
## [289] "IPAQ43" "IPAQ44"
## [291] "IPAQ45" "IPAQ46"
## [293] "IPAQ47" "IPAQ48"
## [295] "IPAQ49" "total_MET"
## [297] "PA_category" "HAND1"
## [299] "HAND2" "HAND3"
## [301] "HAND4" "HAND5"
## [303] "HAND6" "HAND7"
## [305] "HAND8" "HAND9"
## [307] "HAND10" "HAND11"
## [309] "HAND12" "sleep_qual"
## [311] "prescanfullness" "prescanhunger"
## [313] "prescanthirst" "SPSRQ_punishment"
## [315] "SPSRQ_reward" "BISBAS1_rev"
## [317] "BISBAS3_rev" "BISBAS4_rev"
## [319] "BISBAS5_rev" "BISBAS7_rev"
## [321] "BISBAS8_rev" "BISBAS9_rev"
## [323] "BISBAS10_rev" "BISBAS11_rev"
## [325] "BISBAS12_rev" "BISBAS13_rev"
## [327] "BISBAS14_rev" "BISBAS15_rev"
## [329] "BISBAS16_rev" "BISBAS17_rev"
## [331] "BISBAS18_rev" "BISBAS19_rev"
## [333] "BISBAS20_rev" "BISBAS21_rev"
## [335] "BISBAS23_rev" "BISBAS24_rev"
## [337] "bas_drive" "bas_funseeking"
## [339] "bas_rewardresponsiveness" "bas"
## [341] "bis" "DRES"
## [343] "external_eating" "emotional_eating"
## [345] "HAND1_new" "HAND2_new"
## [347] "HAND3_new" "HAND4_new"
## [349] "HAND5_new" "HAND6_new"
## [351] "HAND7_new" "HAND8_new"
## [353] "HAND9_new" "HAND10_new"
## [355] "HAND11_new" "HAND12_new"
## [357] "Handedness_score" "Handedness_cat"
## [359] "TOTAL_KCAL" "TOTAL_G_FAT"
## [361] "TOTAL_G_SUGAR" "PERC_KCAL_FAT"
## [363] "PERC_KCAL_SUG" "PORTIONS_SSB"
## [365] "nback_accuracy" "nback_accuracy_SD"
## [367] "nback_avg_RT" "nback_avg_RT_SD"
## [369] "taste_reinforcers" "reward"
## [371] "reward_expected" "reward_unexpected"
## [373] "punish" "punish_expected"
## [375] "punish_unexpected" "RT_run1"
## [377] "RT_run2" "RT_run3"
## [379] "RT_run4" "RT_ab"
## [381] "RT_cd" "RT_ef"
## [383] "X._correct" "X.correct_run1"
## [385] "X.correct_run2" "X.correct_run3"
## [387] "X.correct_run4" "X.correct_ab_run1"
## [389] "X.correct_ab_run2" "X.correct_ab_run3"
## [391] "X.correct_ab_run4" "trials_AB_run1"
## [393] "trials_AB_run2" "trials_AB_run3"
## [395] "trials_AB_run4" "X.correct_cd_run1"
## [397] "X.correct_cd_run2" "X.correct_cd_run3"
## [399] "X.correct_cd_run4" "trials_cd_run1"
## [401] "trials_cd_run2" "trials_cd_run3"
## [403] "trials_cd_run4" "X.correct_ef_run1"
## [405] "X.correct_ef_run2" "X.correct_ef_run3"
## [407] "X.correct_ef_run4" "trials_ef_run1"
## [409] "trials_ef_run2" "trials_ef_run3"
## [411] "trials_ef_run4" "taste_run1"
## [413] "taste_run2" "taste_run3"
## [415] "taste_run4" "notes"
## [417] "alpha_pos" "alpha_neg"
## [419] "criteria_met" "training_slope"
summary(data0$sensitivity_reward)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.2917 0.4444 0.5000 0.5096 0.5789 0.7500 104
data0$learn[data0$sensitivity_reward < 0.444]<- "didn't learn"
data0$learn[data0$sensitivity_reward >= 0.444 & data0$sensitivity_reward < 0.5 ]<- "meh"
data0$learn[ data0$sensitivity_reward >= 0.5 & data0$sensitivity_reward < 0.57 ]<- "ok"
data0$learn[ data0$sensitivity_reward >= 0.57]<- "pretty pretty good"
summary(as.factor(data0$learn))
## didn't learn meh ok
## 2211 1541 2609
## pretty pretty good NA's
## 2643 104
hmTOTALgood<-ggplot(subset(data0, learn == "pretty pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
geom_tile()+
scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+
geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
hmTOTALgood
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.
## Warning: Removed 115 rows containing missing values (geom_point).
hmTOTALbad<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
geom_tile()+
scale_fill_gradient2(low="blue", high="red", na.value="black", name="")+
geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
hmTOTALbad
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.
## Warning: Removed 116 rows containing missing values (geom_point).
test<-ggarrange(hmTOTALbad,hmTOTALgood,
labels = c("bad", "good"),
ncol = 1, nrow = 2)
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.
## Warning: Removed 116 rows containing missing values (geom_point).
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.
## Warning: Removed 115 rows containing missing values (geom_point).
data0$choice0[data0$choice == "Miss"] <- 0
# good
data0$choice0[data0$choice == "A"] <- 30
data0$choice0[data0$choice == "C"] <- 20
data0$choice0[data0$choice == "E"] <- 10
# bad
data0$choice0[data0$choice == "B"] <- -30
data0$choice0[data0$choice == "D"] <- -20
data0$choice0[data0$choice == "F"] <- -10
hmTOTALgood_flip<-ggplot(subset(data0, learn == "pretty pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=choice0))+
geom_tile()+
scale_fill_gradient2(low="blue", high="red", na.value="black", name="")
#geom_point(aes(shape=as.factor(outcome), size=1, color=as.factor(outcome)))
hmTOTALgood_flip
hmTOTALbad_flip<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=choice0))+
geom_tile()+
scale_fill_gradient2(low="blue", high="red", na.value="black", name="")
#geom_point(aes(shape=as.factor(outcome), size=1, color=as.factor(outcome)))
hmTOTALbad_flip
test<-ggarrange(hmTOTALbad_flip,hmTOTALgood_flip,
labels = c("bad", "good"),
ncol = 1, nrow = 2)
mytable <- xtabs(~choice+learn, data=data0)
ftable(mytable) # print table
## learn didn't learn meh ok pretty pretty good
## choice
## A 362 248 417 460
## B 331 242 420 441
## C 359 236 401 425
## D 353 240 413 377
## E 332 224 410 407
## F 358 270 384 418
## Miss 116 81 164 115
summary(mytable) # chi-square test of indepedence
## Call: xtabs(formula = ~choice + learn, data = data0)
## Number of cases in table: 9004
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 23.464, df = 18, p-value = 0.1734
mytable <- xtabs(~outcome+learn, data=data0)
ftable(mytable) # print table
## learn didn't learn meh ok pretty pretty good
## outcome
## Miss 116 81 164 115
## punish 1032 717 1218 1227
## reward 1063 743 1227 1301
summary(mytable) # chi-square test of indepedence
## Call: xtabs(formula = ~outcome + learn, data = data0)
## Number of cases in table: 9004
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 10.656, df = 6, p-value = 0.09961
mytable <- xtabs(~congruent+learn, data=data0)
ftable(mytable) # print table
## learn didn't learn meh ok pretty pretty good
## congruent
## matched 1489 997 1734 1777
## mismatched 606 463 711 751
## Miss 116 81 164 115
summary(mytable) # chi-square test of indepedence
## Call: xtabs(formula = ~congruent + learn, data = data0)
## Number of cases in table: 9004
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 13.713, df = 6, p-value = 0.03301
There is a difference between the number of mismatched trials in the “learners” and “non learners.” Those who “don’t learn” have more mismatches.